Object detection apparatus and storage medium

ABSTRACT

Important information about an object is detected using less arithmetic processing. An object detection unit generates an edge image from a color image. The object detection unit evaluates symmetry of an image included in the edge image. The object detection unit identifies a symmetry center pixel forming an object having symmetry. The object detection unit detects an object width for each symmetry center pixel. The object detection unit identifies the width of the object in the vertical direction based on the width of the symmetry center pixels in the vertical direction, and identifies the width of the object in the horizontal direction based on the object width identified for each symmetry center pixel.

TECHNICAL FIELD

The present invention relates to an apparatus and a method for detectinga symmetrical object included in an image.

BACKGROUND ART

Some image processing techniques use symmetries of objects. PatentLiterature 1 below describes a technique for evaluating the correlationbetween image areas located right and left to each target pixel bycalculating the correlation between the right and the left of the targetpixel. This technique detects a target pixel for which the calculatedcorrelation is the highest as the center of a symmetrical object.

CITATION LIST Patent Literature

Patent Literature 1: Japanese Unexamined Patent Publication No.2010-267257

DISCLOSURE OF INVENTION Technical Problem

Techniques for detecting objects have many applications in a variety offields. Applications using information about detected objects, whichtypically use information about the position of an object, may furtherneed the size of an object as important information.

However, it has been difficult to obtain information about both theposition and the size of an object included in an image with highaccuracy without increasing the processing load.

In response to the above problem, it is an object of the presentinvention to provide a technique for detecting important informationabout an object (e.g., information about the position and the size of anobject) using less arithmetic processing.

Solution to Problem

To solve the above problem, an object detection apparatus according to afirst aspect of the invention includes an image input unit, an imagefeature quantity extraction unit, and a symmetry evaluation unit.

The image input unit receives an image.

The image feature quantity extraction unit extracts a predeterminedimage feature quantity from the image to generate a feature-quantityextraction image.

The symmetry evaluation unit sets, for every processing target pixel inthe feature-quantity extraction image, a symmetry evaluation area forevaluating symmetry in a first direction in the image in such a mannerthat the symmetry evaluation area is symmetrical with respect to acentral axis in a second direction orthogonal to the first direction,and calculates a weighted value resulting from weighting a valueindicating a correlation value by an image feature quantity for a groupof pixels included in the symmetry evaluation area and locatedsymmetrical with respect to the central axis, while varying a size ofthe symmetry evaluation area to obtain a symmetry evaluation valueindicating a degree of the symmetry in the first direction. Thecorrelation value indicates a correlation between image featurequantities of the group of pixels.

This object detection apparatus obtains the symmetry evaluation valueusing the value resulting from weighting of a value indicating acorrelation between image feature quantities of a group of pixelslocated symmetrical to each other with respect to the central axis basedon the image feature quantities of the group of pixels, and thus canevaluate symmetry in the first direction with high accuracy.

Using the symmetry evaluation value obtained by the object detectionapparatus enables a highly symmetrical object to be detected with highaccuracy using less arithmetic processing.

The width of the symmetry evaluation area in the second direction(orthogonal to the first direction) may be a width corresponding to onepixel, or a width corresponding to a plurality of pixels.

The group of pixels located symmetrical with respect to the central axisis a concept that includes (1) two pixels located at the same distancefrom the central axis in the opposite directions in an image (e.g., twopixels, or a pixel P1 at the distance k from the central axis to theleft in the horizontal direction when the first direction is thehorizontal direction and a pixel P2 at the distance k from the centralaxis to the right in the horizontal direction), or (2) a plurality ofpixels located at the same distance from the central axis in theopposite directions in an image (e.g., n pixels at the distance k fromthe central axis to the left in the horizontal direction when the firstdirection is the horizontal direction, and n pixels at the distance kfrom the central axis to the right in the horizontal direction).

Further, the value indicating the correlation between the image featurequantities is a value indicating the degree of correlation of the imagefeature quantities. The value indicates a higher correlation when, forexample, the correlation between the pixel values of two pixels includedin a feature-quantity extraction image is higher (e.g., the two pixelvalues have a small difference between them or the ratio of the twopixel values is close to 1).

A second aspect of the invention provides the object detection apparatusof the first aspect of the invention in which the symmetry evaluationunit determines a width of the first-direction symmetry based on thesize of the symmetry evaluation area corresponding to the symmetryevaluation value indicating the highest symmetry in the first direction.

This object detection apparatus evaluates symmetry in the firstdirection in the image obtained by extracting the image feature quantityfrom the input image, and thus evaluates symmetry while varying the sizeof the symmetry evaluation area (e.g., the width in the firstdirection). Thus, when determining that a predetermined image area hashigh symmetry, the object detection apparatus can obtain the size of theimage area determined to have high symmetry (e.g., the width in thefirst direction) at the same time. In other words, this object detectionapparatus can detect the position and the size of an object at one timeusing the symmetry of the object included in the image.

Thus, the object detection apparatus can detect a highly symmetricalobject with high accuracy using less arithmetic processing.

A third aspect of the invention provides the object detection apparatusof one of the first and second aspects of the invention in which thesymmetry evaluation unit calculates the symmetry evaluation value forthe target pixel using Formula 1 given below, while varying a value of2w+1, where w is a natural number, and where the symmetry evaluationvalue is larger when a pixel value in the feature-quantity extractionimage is 0 or a positive value and the image feature quantity is larger,Pmax is a predetermined value equal to or greater than a maximum valueof pixel values in the feature-quantity extraction image, P_(i) is apixel value of the target pixel located at coordinates (i, j) in thefeature-quantity extraction image, P_(i−k) is a pixel value of a pixellocated distant from the target pixel by k pixels to a first detectionside that is one side in the first direction, where k is a naturalnumber, and P_(i+k) is a pixel value of a pixel located distant from thetarget pixel by k pixels to a second detection side that is opposite tothe first detection side in the first direction, and 2w+1 is a width ofthe symmetry evaluation area in the first direction.

$\begin{matrix}{\mspace{79mu} {{Formula}\mspace{14mu} 1}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\{ {\left( {{P\; \max} - {{P_{i - k} - P_{i + k}}}} \right)*P_{i - k}*P_{i + k}} \right\}}}} & (1)\end{matrix}$

Through the processing written as Formula 1, this object detectionapparatus obtains the symmetry evaluation value using the valueresulting from weighting a value (Pmax−|P_(i−k)−P_(i+k)|) by a value(P_(i−k)×P_(i+k)) indicating image feature quantities of a group ofpixels. The value (Pmax−|P_(i−k)−P_(i+k)|) indicates a correlationbetween image feature quantities of a group of pixels locatedsymmetrical to each other with respect to the central axis. The objectdetection apparatus can thus evaluate symmetry in the first directionwith high accuracy.

Using the symmetry evaluation value obtained by the object detectionapparatus enables a highly symmetrical object to be detected with highaccuracy using less arithmetic processing.

In the processing written as Formula 1 above, the range of values of thesymmetry evaluation value SYM_(w)(i, j) may be adjusted by gainadjustment (coefficient adjustment), normalization, or clipping to apredetermined value (the processing modified from Formula 1 above may beperformed).

The maximum value Pmax may be a maximum value of possible pixel valuesin the feature-quantity extraction image or may be a predetermined valueequal to or greater than the maximum value.

A fourth aspect of the invention provides the object detection apparatusof one of the first and second aspects of the invention in which thesymmetry evaluation unit calculates the symmetry evaluation value forthe target pixel using Formula 2 given below, while varying a value of2w+1, where w is a natural number, and where the symmetry evaluationvalue is larger when a pixel value in the feature-quantity extractionimage is 0 or a positive value and the image feature quantity is larger,Pmax is a predetermined value equal to or greater than a maximum valueof pixel values in the feature-quantity extraction image, and P_(i, j)is a pixel value of the target pixel located at coordinates (i, j) inthe feature-quantity extraction image, 2w+1 is a width of the symmetryevaluation area in the first direction, 2n+1 is a width of the symmetryevaluation area in the second direction, and d(m) is a predeterminedweighting function.

$\begin{matrix}{\mspace{79mu} {{Formula}\mspace{14mu} 2}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left\lbrack {{d(m)}*\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \begin{Bmatrix}{\left( {{P\; \max} - {{P_{{i - k},m} - P_{{i + k},m}}}} \right)*} \\{P_{{i - k},m}*P_{{i + k},m}}\end{Bmatrix}}} \right\rbrack}}} & (2)\end{matrix}$

The symmetry evaluation unit may also calculate the symmetry evaluationvalue for the target pixel using Formula 3 given below.

$\begin{matrix}{\mspace{79mu} {{Formula}\mspace{14mu} 3}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\lbrack {\left( {{P\; \max} - {\frac{1}{{2\; n} + 1}{{{\sum\limits_{m = {j - n}}^{j + n}\; {{d(m)}*P_{{i - k},m}}} - {\sum\limits_{m = {j - n}}^{j + n}\; {{d(m)}*P_{{i + k},m}}}}}}} \right)*\left\{ {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left( {{d(m)}*P_{{i - k},m}} \right)}} \right\}*\left\{ {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left( {{d(m)}*P_{{i + k},m}} \right)}} \right\}} \right\rbrack}}} & (3)\end{matrix}$

Through the processing written as Formula 2 or 3, this object detectionapparatus obtains the symmetry evaluation value using the average valueobtained by accumulating, in the second direction, the values resultingfrom weighting a value (Pmax−|P_(i−k)−P_(i+k)|) by a value(P_(i−k)×P_(i+k)) indicating image feature quantities of a group ofpixels. The value (Pmax−|P_(i−k)−P_(i+k)|) indicates a correlationbetween image feature quantities of a group of pixels locatedsymmetrical to each other with respect to the central axis. The objectdetection apparatus can thus evaluate symmetry in the first directionwith high accuracy.

Using the symmetry evaluation value obtained by the object detectionapparatus enables a highly symmetrical object to be detected with highaccuracy using less arithmetic processing.

The maximum value Pmax may be a maximum value of possible pixel valuesin the feature-quantity extraction image or may be a predetermined valueequal to or greater than the maximum value.

A fifth aspect of the invention provides the object detection apparatusof the fourth aspect of the invention in which the weighting functiond(m) is

d(m)=1,  (1)

d(m)=n+1−|m−j|, or  (2)

d(m)=c1×exp(−c2×(m−j)̂2),  (3)

where c1 and c2 are predetermined positive coefficients.

This object detection apparatus can perform any intended weighting whenthe values are accumulated in the second direction. When, for example,(1) d(m)=1, the values accumulated in the second direction are weightedby uniform weighting. When (2) d(m)=n+1−|m−j|, the values accumulated inthe second direction are weighted with a larger value closer to thecenter of the symmetry evaluation area in the second direction (with thelargest value when m=j). When (3) d(m)=c1×exp(−c2×(m−j)̂2), the valuesaccumulated in the second direction are weighted with a larger valuecloser to the center of the symmetry evaluation area in the seconddirection (with the largest value when m=j).

A sixth aspect of the invention provides the object detection apparatusof one of the third to fifth aspects of the invention in which thesymmetry evaluation unit obtains a maximum value maxSYM of the symmetryevaluation value SYM_(w)(i, j), and determines the symmetry width basedon the width 2w+1 of the symmetry evaluation area in the first directioncorresponding to the maximum value of the symmetry evaluation valueSYM_(w)(i, j).

When determining that a predetermined image area has high symmetry, theobject detection apparatus can obtain the size of the image areadetermined to have high symmetry (e.g., the width in the firstdirection) at the same time. In other words, this object detectionapparatus can extract the position and the size of an object included inan image at one time using the symmetry of the object.

The object detection apparatus can detect a highly symmetrical objectwith high accuracy using less arithmetic processing.

A seventh aspect of the invention provides the object detectionapparatus of one of the first to sixth aspects of the invention in which(1) when a value of a row i indicating a position of the target pixel inthe first direction is equal to or less than H/2, the symmetryevaluation unit calculates the symmetry evaluation value by varying avalue of w within a range of 1≦w≦(i−1), where w is a half of the widthof the symmetry evaluation area in the first direction and H is thenumber of pixels in the first direction of the image, and H is a naturalnumber, and (2) when the value of the row i indicating the position ofthe target pixel in the first direction is greater than H/2, thesymmetry evaluation unit calculates the symmetry evaluation value byvarying the value of w within a range of 1≦w≦(H−i).

This object detection apparatus ensures that the symmetry evaluationarea is symmetrical with respect to the target pixel, and can detect thesymmetry evaluation value using a maximum area among horizontallysymmetrical areas with respect to the target pixel.

An eighth aspect of the invention provides the object detectionapparatus of the first to seventh aspects of the invention in which theimage feature quantity is an edge intensity of the image.

This object detection apparatus can detect a highly symmetrical objectby using an edge component of an image, and thus can detect an objecthaving high symmetry at the contour (outline) of the object with highaccuracy.

A ninth aspect of the invention provides the object detection apparatusof one of the first to seventh aspects of the invention in which theimage feature quantity is an intensity of a specific color component ofthe image.

This object detection apparatus can detect a highly symmetrical objectby using a specific color component of an object (e.g., a redcomponent), and thus can detect an object having high symmetry for aspecific component with high accuracy.

A tenth third aspect of the invention provides a program enabling acomputer to implement an object detection method. The object detectionmethod includes an image input step, a feature quantity extraction step,and a symmetry evaluation step.

In the image input step, an input image is received.

In the feature quantity extraction step, a predetermined image featurequantity is extracted from the image to generate a feature-quantityextraction image.

In the symmetry evaluation step, a symmetry evaluation area forevaluating symmetry in a first direction in the image is set for everyprocessing target pixel in the feature-quantity extraction image in sucha manner that the symmetry evaluation area is symmetrical with respectto a central axis in a second direction orthogonal to the firstdirection, and a weighted value resulting from weighting a valueindicating a correlation value by an image feature quantity for a groupof pixels included in the symmetry evaluation area and locatedsymmetrical with respect to the central axis is calculated, whilevarying a size of the symmetry evaluation area to obtain a symmetryevaluation value indicating a degree of the symmetry in the firstdirection. The correlation value indicates a correlation between imagefeature quantities of the group of pixels.

The program enabling the computer to implement the object detectionmethod has the same advantageous effects as the object detectionapparatus of the first aspect of the present invention.

Advantageous Effects

The technique of the present invention enables the position and the sizeof an object to be detected based on symmetry of an object in an inputimage, and thus enables important information about an object (e.g.,information about the position and the size of an object) to be detectedusing less arithmetic processing.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram of an object detection system 1000 accordingto a first embodiment.

FIG. 2 is a block diagram of an object detection unit 22 according tothe first embodiment.

FIG. 3 is a diagram showing example images generated in the steps ofobject detection implemented by the object detection system 1000 of thefirst embodiment.

FIG. 4A shows an input image 101 including a symmetrical object.

FIG. 4B shows a luminance image 102 generated by extracting a luminancecomponent from the input image 101.

FIG. 4C shows an edge image 103 generated from the luminance image 102.

FIG. 4D shows a symmetry evaluation map image 104.

FIG. 4E shows a symmetry width map image 105.

FIG. 4F shows a symmetry center map image 106.

FIG. 4G shows a composite image (output image) 107 including asuperimposed area frame 130.

FIG. 5 is a diagram of pixels located right and left to a target pixelP_(i).

FIG. 6 is a diagram describing a method for obtaining a symmetry widthwa.

FIG. 7 is a graph showing (example) changes in the symmetry evaluationmap image 104 in the horizontal direction.

FIG. 8A is a diagram showing example images generated in the steps ofobject detection implemented by an object detection system according toa second embodiment.

FIG. 8B shows a Cr-component image 202 generated by extracting a Crcomponent from the input image 101.

FIG. 8C shows a feature-quantity extraction image (an R-component image,or a Cr-component enhanced image) 203.

FIG. 8D shows a symmetry evaluation map image 204.

FIG. 8E shows a symmetry width map image 205.

FIG. 8F shows a symmetry center map image 206.

FIG. 8G shows a composite image (an output image) 207 including asuperimposed area frame 230.

DESCRIPTION OF THE PREFERRED EMBODIMENTS First Embodiment

A first embodiment will now be described with reference to the drawings.

1.1 Structure of Object Detection System

FIG. 1 is a block diagram of an object detection system 1000 accordingto a first embodiment. The object detection system 1000 includes animaging apparatus 1, an object detection apparatus 2, and a displayapparatus 3.

The imaging apparatus 1 includes an optical system (not shown) and animage sensor. The optical system focuses light from a subject. The imagesensor, which may be, for example, a charge-coupled device (CCD) imagesensor or a complementary metal oxide semiconductor (CMOS) image sensor(not shown), converts the light focused through the optical system toimage signals (electrical signals) by photoelectric conversion. Theimaging apparatus 1 outputs the image captured by the image sensor(image signals) to the object detection apparatus 2.

The object detection apparatus 2 receives image signals output from theimaging apparatus 1, and detects a symmetrical object included in animage formed using the input image signals, and outputs an image (imagesignals) indicating the detection result to the display apparatus 3.

In one example, the object detection apparatus 2 is mounted on avehicle. When mounted on the front side of the vehicle, the imagingapparatus 1 captures an image (scene) of the environment in front of thevehicle. Alternatively, the imaging apparatus 1 may be mounted on therear side of the vehicle to capture an image (scene) of the environmentbehind the vehicle.

The display apparatus (monitor) 3 is mounted on, for example, thedashboard of the vehicle. The display apparatus 3 may also function as amonitor for a navigation system.

As shown in FIG. 1, the object detection apparatus 2 includes an imageinput unit 21, an object detection unit 22, and a superimposing unit 23.

The image input unit 21 receives an image (image signals) output fromthe imaging apparatus 1. When, for example, the imaging apparatus 1includes a CCD image sensor including an RGB Bayer array of colorfilters, the image input unit 21 receives a sequence of pixel signalsincluding an R-component signal, a G-component signal, and a B-componentsignal. The image input unit 21 converts the input image signals tosignals in a predetermined format as appropriate, and outputs theresulting image signals (unconverted image signals when no conversion isperformed) to the object detection unit 22. The image input unit 21outputs the input image signals to the superimposing unit 23.

The conversion into a predetermined format refers to, for example,conversion from one color space to the other (e.g., conversion from theRGB color space to the YCbCr color space). The image input unit 21converts, as appropriate, the input image signals defined in the RGBcolor space (an R-component signal, a G-component signal, and aB-component signal) to, for example, signals defined in the YCbCr colorspace (a Y-component signal, a Cb-component signal, and a Cr-componentsignal).

In the example described below, for ease of explanation, the input imagesignals in the RGB color space (an R-component signal, a G-componentsignal, and a B-component signal) are converted to signals in the YCbCrcolor space (a Y-component signal, a Cb-component signal, and aCr-component signal) in the image input unit 21.

The object detection unit 22 receives an image (image signals) outputfrom the image input unit 21, and subjects the input image topredetermined image processing to detect a symmetrical object includedin an image formed using the image signals (e.g., a frame image)(identifies an image area corresponding to a symmetrical object). Theobject detection unit 22 outputs the detection result (e.g., informationidentifying an image area corresponding to a symmetrical object) to thesuperimposing unit 23.

As shown in FIG. 2, the object detection unit 22 includes an imagefeature quantity extraction unit 221, a symmetry evaluation unit 222, acenter detection unit 223, and an object area detection unit 224.

The image feature quantity extraction unit 221 calculates (extracts) animage feature quantity from an image output from the image input unit21. More specifically, the image feature quantity extraction unit 221performs differential arithmetic processing using the Sobel filter andextracts, for example, an edge component as an image feature quantityfrom a Y-component image (luminance component image) formed using aY-component signal (luminance component signal) output from the imageinput unit 21. The image feature quantity extraction unit 221 outputs animage in which each pixel has the extracted edge component as its pixelvalue (feature-quantity extraction image) to the symmetry evaluationunit 222 and the object area detection unit 224 as an imagefeature-quantity extraction image.

For ease of explanation, the pixel value of each pixel in thefeature-quantity extraction image is hereafter larger as the imagefeature quantity to be extracted is larger.

The symmetry evaluation unit 222 receives an image (feature-quantityextraction image) extracted from the image feature quantity extractionunit 221. For the feature-quantity extraction image, the symmetryevaluation unit 222 evaluates (determines) symmetry of each pixel in apredetermined direction (e.g., the horizontal direction) in an image,and determines (estimates) the width of an image area (width in thepredetermined direction, or for example the horizontal direction). Thesymmetry evaluation unit 222 obtains symmetry evaluation map dataindicating the correspondence for each pixel between informationidentifying each pixel (processing target pixel) (e.g., the coordinatesof each pixel) and a value indicating the degree of symmetry evaluated(determined) for the pixel (or a value correlated with this value).

Further, the symmetry evaluation unit 222 obtains symmetry width mapdata indicating the correspondence for each pixel between informationidentifying each pixel (processing target pixel) (e.g., the coordinatesof each pixel) and a value indicating the width of a highly symmetricalimage area evaluated (determined) for the pixel (the width in thepredetermined direction, or for example the horizontal direction) (or avalue correlated with this value).

The symmetry evaluation unit 222 outputs the obtained symmetryevaluation map data to the center detection unit 223. The symmetryevaluation unit 222 also outputs the obtained symmetry width map data tothe object area detection unit 224.

The center detection unit 223 receives the symmetry evaluation map dataobtained by the symmetry evaluation unit 222. The center detection unit223 identifies a pixel or a pixel area having a maximum local value (ora value close to a maximum local value) in the predetermined directionin the image (e.g., the horizontal direction) when the symmetryevaluation map data is a two-dimensional image (an image generatedthrough mapping of symmetry evaluation values serving as the pixelvalues of the pixels), and determines (estimates) the position of thecentral axis of the highly symmetrical object based on the identifiedpixel or pixel area. The center detection unit 223 then outputsinformation about the position of the central axis (in the image) of thedetermined (estimated) highly symmetrical object to the object areadetection unit 224.

The object area detection unit 224 receives the symmetry width map dataobtained by the symmetry evaluation unit 222, and the information aboutthe position of the central axis (in the image) of the highlysymmetrical object output from the center detection unit 223. The objectarea detection unit 224 detects the highly symmetrical object based onthe symmetry width map data, and the information about the position ofthe central axis (in the image) of the highly symmetrical object, andidentifies an area corresponding to the detected highly symmetricalobject in the image. The object area detection unit 224 then outputsinformation about the identified image area corresponding to the highlysymmetrical object in the image to the superimposing unit 23.

The superimposing unit 23 receives the image output from the image inputunit 21, and the information identifying the image area corresponding tothe detected highly symmetrical object in the image output from theobject area detection unit 224 of the object detection unit 22. Thesuperimposing unit 23 generates (superimposes) an image indicating theimage area corresponding to the highly symmetrical object on the imageoutput from the image input unit 21 based on the information about theimage area corresponding to the detected highly symmetrical objectoutput from the object area detection unit 224. The superimposing unit23 generates (superimposes) an image of a rectangular frame indicatingthe image area corresponding to the highly symmetrical object on theimage output from the image input unit 21. The superimposing unit 23then outputs the resulting composite image to the display apparatus 3.

The display apparatus 3 receives the image output from the superimposingunit 23 of the object detection apparatus 2, and displays the image.

1.2 Operation of Object Detection System

The operation of the object detection system 1000 with theabove-described structure will now be described with reference to thedrawings.

FIG. 3 shows example images generated in the steps of object detectionimplemented by the object detection system 1000.

FIG. 4A shows a captured image 101 captured by the imaging apparatus 1and input into the object detection apparatus 2.

As shown in FIG. 4A, the captured image 101 includes a vehicle 110 as asubject. The vehicle 110 includes right and left tail lamps 111R and111L. The right and left tail lamps 111R and 111L are arrangedsymmetrical to each other with respect to a central axis 112 in thewidth direction of the vehicle 110.

In the example described below, the captured image 101 shown in FIG. 4Ais captured by the imaging apparatus 1, and the captured image 101 isprocessed by the object detection apparatus 2.

The captured image 101 (an image signal forming the captured image 101)obtained by the imaging apparatus 1 is input into the image input unit21 of the object detection apparatus 2. The captured image 101 is formedby the R-component signal, the G-component signal, and the B-componentsignal.

The image input unit 21 subjects the input captured image to color spaceconversion. More specifically, the image input unit 21 converts the RGBcolor space to, for example, the YCbCr color space to form the capturedimage 101. The R-component signal, the G-component signal, and theB-component signal are converted to the Y-component signal, theCb-component signal, and the Cr-component signal.

The image input unit 21 outputs a Y-image (luminance image) formed bythe Y-component signal (luminance signal) to the image feature quantityextraction unit 221 of the object detection unit 22. The image inputunit 21 outputs the input captured image to the superimposing unit 23.

FIG. 4B shows a Y-image (luminance image) 102 obtained by the imageinput unit 21.

The color space conversion performed by the image input unit 21 shouldnot be limited to the above-described process, but may be, for example,conversion from the RGB color space to another color space, such as theLab color space and the YPbPr color space.

Alternatively, the luminance image 102 may be generated by using theG-signal of the RGB color space. For pixels having the R and Bcomponents, interpolation can generate the G signal.

The color space processing may be performed by using a memory (notshown) such as a frame memory that can store image signals.

The image feature quantity extraction unit 221 subjects the Y image(luminance image) 102 obtained by the image input unit 21 to theprocessing to calculate (extract) an image feature quantity. In thepresent embodiment, a physical quantity correlated with an edgecomponent of luminance is used as an image feature quantity.

More specifically, the image feature quantity extraction unit 221 in thepresent embodiment subjects the luminance image 102 to an edge detectionprocess to generate a feature-quantity extraction image (edge image)103.

FIG. 4C shows a feature-quantity extraction image (edge image) 103obtained by the image feature quantity extraction unit 221. The imagefeature quantity extraction unit 221 subjects the luminance image 102to, for example, differential arithmetic processing (e.g., filteringusing the Sobel filter) to generate the feature-quantity extractionimage (edge image) 103.

Subsequently, the symmetry evaluation unit 222 evaluates the symmetry ofthe edge image 103 obtained by the image feature quantity extractionunit 221. A method for evaluating the symmetry will now be described.

1.2.1 Method for Evaluating Symmetry

The symmetry evaluation unit 222 evaluates the symmetry with respect toa target pixel P_(i) shown in FIG. 5. The target pixel P_(i) is a pixelincluded in the edge image 103. The target pixel P_(i) is at thecoordinates (i, j) in the edge image 103. The pixel P_(x) hereafterrefers to a pixel at the coordinates (x, j) in the edge image 103. Morespecifically, the pixel P_(x) is in the x-th row in the horizontaldirection and in the j-th column in the vertical direction. In theformula, P_(x) is the pixel value of the pixel P_(x). In the presentembodiment, P_(x) is a value ranging from 0 to 255. The value P_(x) islarger as the image feature quantity (edge component quantity in thepresent embodiment) is larger (the degree of the target image featurequantity is higher).

FIG. 5 shows w pixels (P_(i−w) to P_(i−l)), which are located left tothe target pixel P_(i), and w pixels (P_(i+l) to P_(i+w)) (w is anatural number), which are located right to the target pixel P_(i). Thearithmetic operation corresponding to Formula 4 is used to evaluate thesymmetry of the (2_(w+1)) pixels (P_(i−w) to P_(i+w)).

$\begin{matrix}{{Formula}\mspace{14mu} 4} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\{ {\left( {255 - {{P_{i - k} - P_{i + k}}}} \right)*P_{i - k}*P_{i + k}} \right\}}}} & (4)\end{matrix}$

In Formula 4, SYM_(w)(i, j) is an evaluation value for the symmetry, andk is an integer ranging from 1 to w.

The pixel P_(i−k) and the pixel P_(i+k) are at positions symmetrical toeach other with respect to the target pixel P_(i). When the pixelP_(i−k) and the pixels P_(i+k) have the same pixel value, the differencebetween the pixels, or the value |P_(i−k)−P_(i+k)|, is a minimum valueof 0. As a result, the value (255−|P_(i−k)−P_(i+k)|) is a maximum valueof 255.

The evaluation value (255−|P_(i−k)−P_(i+k)|) is multiplied by the pixelvalues P_(i−k) and P_(i+k). As a result, the value is weighted by theimage feature quantity of pixels located distant from the target pixelP_(i) by k pixels to the right and to the left in the horizontaldirection. This excludes pixels having small image feature quantitiesfrom the target pixels for symmetry evaluation. More specifically, whenthe feature-quantity extraction image is an edge image 103, the pixelsP_(i−k) and P_(i+k), which do not form edges in the edge image 103, havea value close to 0. For areas that do not form edges, the resultingvalue (255−|P_(i−k)−P_(i+k)|)×P_(i−k)×P_(i+k) would be close to 0.

In Formula 4, the evaluation value SYM_(w)(i, j) is larger as thesymmetry is higher for a target image feature quantity (an edgecomponent quantity in this embodiment).

The symmetry evaluation unit 222 calculates the evaluation valuesSYM_(w)(i, j) for each target pixel P_(i) by varying the width w. Thesymmetry evaluation unit 222 calculates the maximum value maxSYM of theevaluation values SYM_(w)(i, j) for the target pixel P_(i) as given inFormula 5 below.

Formula 5

max SYM=max(SYM₁(i,j),SYM₂(i,j), . . .,SYM_(N−1)(i,j),SYM_(N)(i,j))  (5)

In Formula 5, N is a maximum value of the width w. The maximum value Nis i−1 when the row value i, which indicates the horizontal position ofthe target pixel, is H/2 or less, where H is the number of pixels in thehorizontal direction of the edge image 103. The maximum value N is H−iwhen the row value i indicating the horizontal position of the targetpixel is greater than H/2.

In Formula 5, max( ) is a function to return a maximum value of anelement. The processing written as Formula 5 yields the maximum valuemaxSYM as a maximum one of the values SYM1(i, j) to SYMN(i, j).

The symmetry evaluation unit 222 obtains the width w that returns themaximum value maxSYM as the symmetry width wa. More specifically, theevaluation value SYM_(w)(i, j) is the maximum value maxSYM when w=wa.The maximum value maxSYM can be written as the formula below using thesymmetry width wa.

max SYM=SYMwa(i,j)

The processing written as Formula 4 and Formula 5 above will now bedescribed with reference to FIG. 6. FIGS. 6( a) to 6(f) are schematicdiagrams showing examples of feature-quantity extraction images, whichare obtained by extracting feature quantities from a captured image of ahorizontally symmetrical object having an axis C1 serving as the axis ofsymmetry. In FIGS. 6( a) to 6(f), an area R1 is a target area for thecalculation corresponding to Formula 4. For ease of explanation, eachpixel included in white portions in FIGS. 6( a) to 6(f) is assumed tohave an image feature quantity (pixel value) P_(x) of 255, whereas eachpixel included in black portions is assumed to have an image featurequantity P_(x) of 0. The case of performing processing with coefficientadjustment written as Formula 6 below to allow the value of SYM_(w)(i,j)to fall within a range from 0 to 255 will now be described.

In FIGS. 6( a) to 6(f), the proportion of pixels having an image featurequantity (pixel value) P_(x) of 255 in the area R1 is calculated in themanner described below.

In the example of FIG. 6( a), the proportion of pixels having an imagefeature quantity (pixel value) P_(x) of 255 in the area R1 is 0.0.

In the example of FIG. 6( b), the proportion of pixels having an imagefeature quantity (pixel value) P_(x) of 255 in the area R1 is 0.2.

In the example of FIG. 6( c), the proportion of pixels having an imagefeature quantity (pixel value) P_(x) of 255 in the area R1 is 0.4.

In the example of FIG. 6( d), the proportion of pixels having an imagefeature quantity (pixel value) P_(x) of 255 in the area R1 is 0.6.

In the example of FIG. 6( e), the proportion of pixels having an imagefeature quantity (pixel value) P_(x) of 255 in the area R1 is 0.4.

In the example of FIG. 6( f), the proportion of pixels having an imagefeature quantity (pixel value) P_(x) of 255 in the area R1 is 0.3.

$\begin{matrix}{{Formula}\mspace{14mu} 6} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\{ {\left( {255 - {{P_{i - k} - P_{i + k}}}} \right)*\frac{P_{i - k}}{255}*\frac{P_{i + k}}{255}} \right\}}}} & (6)\end{matrix}$

In the example of FIG. 6( a), each pixel included in the calculationtarget area R1 has an image feature quantity (pixel value) of 0. Thus,SYM_(w)(i, j) is calculated as SYM_(w)(i, j)=0 using Formula 6.

In the example of FIG. 6( b), the proportion of pixels having an imagefeature quantity (pixel value) of 255 in the calculation target area R1is 0.2. In this case, the value SYM_(w)(i, j) is calculated in themanner described below using Formula 6.

SYM_(w)(i,j)=0.2×255.

In the example of FIG. 6( c), the proportion of pixels having an imagefeature quantity (pixel value) of 255 in the calculation target area R1is 0.4. Thus, the value of SYM_(w)(i, j) is determined as describedbelow using Formula 6.

SYM_(w)(i,j)=0.4×255.

In the example of FIG. 6( d), the proportion of pixels having an imagefeature quantity (pixel value) of 255 in the calculation target area R1is 0.6. Thus, the value of SYM_(w)(i, j) is determined as describedbelow using Formula 6.

SYM_(w)(i,j)=0.6×255.

In the example of FIG. 6( e), the proportion of pixels having an imagefeature quantity (pixel value) of 255 in the calculation target area R1is 0.4. Thus, the value of SYM_(w)(i, j) is determined as describedbelow using Formula 6.

SYM_(w)(i,j)=0.4×255.

In the example of FIG. 6( f), the proportion of pixels having an imagefeature quantity (pixel value) of 255 in the calculation target area R1is 0.3. Thus, the value of SYM_(w)(i, j) is determined as describedbelow using Formula 6.

SYM_(w)(i,j)=0.3×255.

As described above, with the SYM_(w)(i, j) being a maximum value in thestate shown in FIG. 6( d), the evaluation value SYM_(w)(i, j) in thestate shown in FIG. 6( d) (=0.6×255) is the maximum value maxSYM, andthe corresponding width w (w in the state shown in FIG. 6( d)) is usedas the symmetry width wa. In other words, the proportion of pixelshaving an image feature quantity (pixel value) of 255 in the state shownin FIG. 6( d) is maximum. Thus, in processing the feature-quantityextraction images shown in FIG. 6, the symmetry evaluation unit 222obtains the evaluation value SYM_(w)(i, j) (=0.6×255) in the state shownin FIG. 6( d) as the value maxSYM. The symmetry evaluation unit 222obtains the width w in this state (w in the state shown in FIG. 6( d))as the symmetry width wa.

The symmetry evaluation unit 222 performs the processing written asFormulae 4 and 5 for each of all the target pixels for which symmetry isto be evaluated, and generates (obtains) (1) symmetry evaluation mapdata showing the correspondence between each processing target pixel andthe corresponding value maxSYM (or a value correlated with the valuemaxSYM), and (2) symmetry width map data showing the correspondencebetween each processing target pixel and the corresponding symmetrywidth wa (the width w that returns the maximum value maxSYM) (or a valuecorrelated with the symmetry width wa).

FIG. 4D shows a symmetry evaluation map image 104 obtained from thesymmetry evaluation map data. The symmetry evaluation map image 104 isan image in which each pixel has the corresponding value maxSYM (or avalue correlated with the value maxSYM) as its pixel value.

FIG. 4E shows a symmetry width map image 105 obtained from the symmetrywidth map data. The symmetry width map image 105 is an image in whicheach pixel has the corresponding symmetry width wa (the width w thatreturns the maximum value maxSYM) (or a value correlated with thesymmetry width wa) as its pixel value.

In the present embodiment, the symmetry evaluation unit 222 uses each ofall pixels forming the edge image 103 as a target pixel for whichsymmetry is to be evaluated (evaluation target pixel). In other words,the symmetry evaluation unit 222 calculates the value SYM_(w)(i, j) foreach of all pixels included in the edge image 103, and then calculatesthe corresponding maximum value maxSYM.

To reduce the arithmetic processing or to improve the processing speed,the symmetry evaluation unit 222 may use selected pixels in the edgeimage 103 (thinning of pixels) and perform the same processing asdescribed above to obtain the symmetry evaluation map data and thesymmetry width map data. For example, the symmetry evaluation unit 222may use only pixels in odd-numbered lines or pixels in even-numberedlines in the horizontal (or vertical) direction as pixels for whichsymmetry is to be evaluated (evaluation target pixels). Alternatively,the symmetry evaluation unit 222 may use fewer pixels, or pixelsselected at every three lines, as evaluation target pixels.

The symmetry evaluation map data is map data having the value maxSYM (ora value correlated with the value maxSYM) calculated for each evaluationtarget pixel as its map element. The symmetry evaluation map data canalso be seen as an image in which each evaluation target pixel has itscorresponding calculated maximum value maxSYM (or a value correlatedwith the value maxSYM) as the pixel value. FIG. 4D shows an imagerepresenting the symmetry evaluation map data (the symmetry evaluationmap image 104). The symmetry evaluation unit 222 only needs to obtainthe value maxSYM calculated for each evaluation target pixel (or a valuecorrelated with the value maxSYM). The symmetry evaluation unit 222 maynot obtain the image (the symmetry evaluation map image 104) in theformat shown in FIG. 4D. The symmetry evaluation unit 222 only needs toobtain data showing the correspondence between each evaluation targetpixel and its maximum value maxSYM.

The present embodiment uses each of all pixels forming the edge image103 as an evaluation target pixel. In this case, the symmetry evaluationmap image 104 is a grayscale image in which the pixel value of each ofall the pixels of the edge image 103 is replaced with the correspondingvalue maxSYM (or a value correlated with the value maxSYM).

The value SYM_(w)(i, j) calculated by Formula 4 above can be 24-bitdata. Although the symmetry evaluation map image 104 may use the maximumvalue maxSYM directly as the pixel value of each pixel, the symmetryevaluation map image 104 may be an image obtained through dynamic rangeconversion. More specifically, dynamic range conversion may be performedto adjust the range of values maxSYM, which is defined by a minimumvalue to a maximum value of the values maxSYM calculated for an inputimage of one frame, to, for example, the range of 0 to 255 (the 8-bitrange) (the processing may be other processing, such as normalization,clipping to a predetermined value, or gain adjustment such as theprocessing written as Formula 6).

When the value SYM_(w)(i, j) is adjusted to fall within the range of 0to 255, each element of the symmetry evaluation map image 104 can have avalue within the range of 0 to 255. In FIG. 4D, a color closer to whiteindicates a larger value SYM_(w)(i, j) (a value closer to 255). Morespecifically, in FIG. 4D, a color closer to white represents an areaevaluated to have high symmetry, indicating that the area is near thecenter of a symmetrical object. In FIG. 4D, a color closer to blackrepresents an area having low symmetry. The dynamic range conversiondescribed above is a mere example. Other dynamic range conversion may beperformed to convert the value SYM_(w)(i, j) into a range other than therange of 0 to 255.

The symmetry evaluation unit 222 generates (obtains) the symmetry widthmap data for all evaluation target pixels for which symmetry is to beevaluated. The symmetry width map data is map data having the symmetrywidth wa that returns the maximum value maxSYM for each evaluationtarget pixel as its map element. The symmetry width map data can also beseen as an image in which each evaluation target pixel has thecorresponding symmetry width wa as its pixel value. FIG. 4E shows animage representing the symmetry width map data (the symmetry width mapimage 105). The present embodiment uses each of all pixels forming theedge image 103 as an evaluation target pixel. In this case, the symmetrywidth map image 105 is a grayscale image in which the pixel value ofeach of all the pixels of the edge image 103 is replaced with thecorresponding symmetry width wa.

As described above in the present embodiment, the symmetry width wa canbe a value ranging from 1 to N. In this case, each element of thesymmetry width map image 105 can be a value ranging from 1 to N. Thevalue N differs depending on the pixel position. In FIG. 3E, a colorcloser to white indicates an area with a larger symmetry width wa. InFIG. 3E, a color closer to black indicates an area with a smallersymmetry width wa.

Subsequently, the center detection unit 223 refers to the symmetryevaluation map data (symmetry evaluation map image 104) generated by thesymmetry evaluation unit 222, and generates symmetry center map data(symmetry center map image 106). The center detection unit 223determines (estimates) a pixel corresponding to a maximum local point ofthe values maxSYM (or a group of pixels around the maximum local point)for each horizontal line in the symmetry evaluation map image 104.

FIG. 7 is a graph showing changes in the symmetry evaluation map image104 in the horizontal direction. More specifically, FIG. 7 shows thechanging values maxSYM for one horizontal line of the symmetryevaluation map image 104.

In FIG. 7, three pixels in rows 330 to 332 in the horizontal direction(i coordinates) (pixels at i=330, i=331, and i=332) have local maximumsymmetry evaluation values (values maxSYM). In this case, the centerdetection unit 223 determines that three pixels at i=330, 331, and 332correspond to a local maximum point (or an area around a local maximumpoint) of the symmetry evaluation values (values maxSYM) for onehorizontal line shown in FIG. 7.

In the same manner, the center detection unit 223 identifies (estimates)an area (a pixel or a group of pixels) corresponding to a local maximumpoint (or an area around a local maximum point) for each of allhorizontal lines. The area identified (estimated) by the centerdetection unit 223 is referred to as a “symmetry center pixel area.”

FIG. 4F shows a symmetry center map image 106 indicating a symmetrycenter pixel area identified (estimated) by the center detection unit223. The symmetry center map image 106 is seen as an image in which, forexample, a pixel determined to be within a symmetry center pixel areahas an element (pixel value) of 1 and a pixel determined not to bewithin the symmetry center pixel area has an element (pixel value) of 0.In FIG. 4F, a white portion indicates a pixel determined to form thesymmetry center pixel area.

The determination as to whether a processing target pixel (target pixel)forms the symmetry center pixel area may be accompanied by, for example,the processing described below.

(1) The pixel value of each pixel forming the symmetry evaluation mapimage 104 is compared with a predetermined threshold. The pixel isdetermined to be a candidate for a local maximum point only when thepixel value exceeds the threshold.

(2) The symmetry evaluation map image 104 is subjected to smoothing inthe horizontal direction (a processing target horizontal line issubjected to smoothing). After smoothing, the resulting image is used toidentify the position of the local maximum point (the horizontalposition).

This eliminates local maximum points resulting from minor variations,and thus yields a highly accurate symmetry center map image.

Subsequently, the object area detection unit 224 detects the horizontalwidth and the vertical width of a symmetrical object included in theinput image.

The object area detection unit 224 detects the horizontal width of theobject using the symmetry center map data (the symmetry center map image106) and the symmetry width map data (the symmetry width map image 105).

More specifically, the object area detection unit 224 detects thehorizontal width of an object through, for example, the processing (1)to (5) described below. For ease of explanation, a horizontal lineincluding the first pixel having a pixel value of 1 in the symmetrycenter map image 106 is referred to as the j-th horizontal line.

(1) In the symmetry center map image 106, a pixel forming the centralaxis of a symmetrical object is given a pixel value of 1. Thus, theobject area detection unit 224 extracts a pixel having a pixel value of1 from the symmetry center map image 106, and obtains the symmetry widthof the extracted pixel from the symmetry width map image 105.

(1A):

When a single pixel having a pixel value of 1 is detected in thehorizontal direction (without a sequence of pixels being detected in thehorizontal direction), the object area detection unit 224 extracts thesymmetry width W(i, j) of the extracted single pixel having the pixelvalue of 1 (the pixel is at the coordinates (i, j)) from the symmetrywidth map data.

(1B):

When a plurality of pixels having a pixel value of 1 are sequentiallydetected in the horizontal direction, the average of the symmetry widthvalues for the plurality of pixels (sequential pixels in the horizontaldirection) is used as the symmetry width. In one example, three pixelsat the coordinates (i−1, j), (i, j), and (i+1, j) each having a pixelvalue of 1 have the symmetry width values W(i−1, j), W(i, j), and W(i+1,j), respectively. In this case, the symmetry width W(i, j) can becalculated in the manner described below.

W(i,j)=AVRG(W(i−1,j),W(i,j),W(i+1,j)), or

W(i,j)=MAX(W(i−1,j),W(i,j),W(i+1,j)).

In these formulae, AVRG( ) is a function to return the average for anelement, and MAX( ) is a function to return a maximum value of anelement.

The symmetry width W(i, j) calculated for the j-th horizontal linethrough the processing (1A) or (1B) is referred to as W(i0, j).

(2) When, for example, the (j+1)th horizontal line includes a pixelhaving a pixel value of 1 substantially at the same position as thepixel extracted through the processing (1) in the horizontal direction(e.g., a pixel located at coordinates within the range of (i−a, j+1) to(i+a, j+1), where “a” is a predetermined threshold used to determinewhether the positions in the horizontal direction are substantially thesame), the symmetry width of the pixel is extracted from the symmetrywidth map data in the same manner as with the processing (1).

When the pixel (i₁, j+1) has a pixel value of 1 (or the pixel is at thecentral position in the sequence of pixels having a pixel value of 1 inthe horizontal line), the object area detection unit 224 calculates thesymmetry width W(i₁, j) of the pixel (i₁, j+1) in the same manner aswith the processing (1).

(3) For the (j+2)th and subsequent horizontal lines, the same processingas described above is repeated.

The above processing is repeated until the horizontal line includes nopixel having a pixel value of 1 substantially at the same position asthe pixel extracted through the processing (1) in the horizontaldirection (e.g., a pixel located at coordinates within the range of(i−a, j+1) to (i+a, j+1), where “a” is a predetermined threshold used todetermine whether the horizontal positions are substantially the same).

(4) The object area detection unit 224 calculates a maximum value maxWof the symmetry width calculated through the processing (1) to (3)described above. More specifically, the object area detection unit 224performs the processing written as Formula 7 below to calculate themaximum value maxW of the symmetry width.

In Formula 7, the j-th to (j+m−1)th horizontal lines each include apixel having a pixel value of 1 at substantially the same position asthe pixel extracted with the processing (1) in the horizontal direction(e.g., a pixel located at coordinates within the range of (i−a, j+1) to(i+a, j+1), where “a” is a predetermined threshold used to determinewhether the horizontal positions are substantially the same).

Formula 7

maxW=max(W(i ₀ ,j),W(i ₁ ,j+1), . . . ,W(i _(m−2) ,j+m−2),W(i _(m−1),j+m−1))  (7)

(5) The object area detection unit 224 detects the maximum value maxW ofthe calculated symmetry width values as the width of the object (thedistance from the center of the object in the horizontal direction toone end of the object). In FIG. 4F, the symmetry width is at the maximumin the (j+k1)th horizontal line. In other words, maxW=W(i_(k1), j+k1) inthis example.

The object area detection unit 224 detects the width of the object inthe vertical direction using the width (length) of the symmetry centerpixel area in the vertical direction. More specifically, the object areadetection unit 224 identifies the upper end of symmetry center pixelsarranged sequentially in the vertical direction as the upper end of theobject, and identifies the lower end of the symmetry center pixelsarranged sequentially in the vertical direction as the lower end of theobject.

As shown in FIG. 4F, the object area detection unit 224 determines(identifies) an area R1 defined as a rectangular area with the upperleft vertex at the coordinates (i_(k1)−maxW, j) and the lower rightvertex at the coordinates (i_(k1)+maxW, j+m−1) as an image areacorresponding to a highly symmetrical object when, for example, theupper end of symmetry center pixels arranged sequentially in thevertical direction is in the j-th horizontal line, the lower end ofsymmetry center pixels arranged sequentially in the vertical directionis in the (j+m−1)th horizontal line, and the central position of thepixels in the horizontal line corresponding to the value maxW(=W(j_(k1),j+k1)) is at the coordinates (i_(k1), j+k1).

The object area detection unit 224 outputs information indicating animage area corresponding to the identified object (the highlysymmetrical object) (information identifying the area R1, or for exampleinformation indicating the coordinates of the rectangular area) to thesuperimposing unit 23.

Although the above embodiment describes the case in which the objectarea detection unit 224 identifies the image area corresponding to thehighly symmetrical object using a rectangular area, the embodimentshould not be limited to this structure. For example, the object areadetection unit 224 may identify an image area corresponding to a highlysymmetrical object based on a symmetry width extracted for eachhorizontal line from the symmetry width map data. In this case, theobject area detection unit 224 identifies an area that extends to theright and to the left of the symmetry center pixel (the pixel includedin the symmetry center pixel area) each by a length corresponding to thesymmetry width for each horizontal line and determines the identifiedarea as an image area corresponding to the highly symmetrical object.The resulting image area corresponding to the highly symmetrical objecthas substantially the same shape as the highly symmetrical object (e.g.,the image area is shaped substantially in conformance with the contourof the vehicle 110 shown in FIG. 4A).

The superimposing unit 23 generates (superimposes) an image indicatingan image area corresponding to the highly symmetrical object on theimage output from the image input unit 21 (input image 101) based oninformation identifying an image area corresponding to the detectedhighly symmetrical object output from the object area detection unit224. The superimposing unit 23 generates (superimposes) an image inwhich, for example, a rectangular frame indicating the image areacorresponding to the highly symmetrical object appears on the imageoutput from the image input unit 21. The superimposing unit 23 thenoutputs the resulting composite image to the display apparatus 3.

The display apparatus 3 displays the image indicating the image areacorresponding to the highly symmetrical object output from thesuperimposing unit 23 of the object detection apparatus 2. FIG. 3G showsa composite image 107 in which the area frame 130 indicating the area ofthe vehicle 110, which is a symmetrical object, is superimposed on theinput image 101. When the width of the object is determined for eachhorizontal direction line, the area frame would not be rectangular butwould be shaped in conformance with the contour of the object (shapedsubstantially in conformance with the contour of the vehicle 110).

As described above, the object detection system 1000 of the presentembodiment evaluates the symmetry of an image obtained by extracting animage feature quantity from the input image (captured image) by varyingthe width (width in the predetermined direction, which is for examplethe horizontal direction) to evaluate the symmetry of the image in thepredetermined direction (horizontal direction). As a result, when theobject detection system 1000 of the present embodiment determines that apredetermined image area has high symmetry, the object detection system1000 can simultaneously obtain the width in the predetermined direction(horizontal direction) of the image area for which the symmetry is high.In other words, the object detection system 1000 of the presentembodiment can extract the position and the size of an object at onetime using the symmetry of the object included in the image.

The object detection system 1000 of the present embodiment can detectthe central axis of a highly symmetrical object using evaluation datafor symmetry in a predetermined direction (horizontal direction). Theobject detection system 1000 of the present embodiment can thus detect ahighly symmetrical object with high accuracy using less arithmeticprocessing.

First Modification

A first modification of the present embodiment will now be described.

An object detection system according to this modification has the samestructure as described in the first embodiment. The modification will bedescribed focusing on its differences from the first embodiment. Thecomponents that are the same as in the first embodiment are given thesame reference numerals as those embodiments and will not be describedin detail.

The symmetry evaluation unit 222 in this modification performs theprocessing written as Formula 8 instead of Formula 4 described above.Formula 4 uses an image area of one horizontal line as an area for whicha symmetry evaluation value is to be calculated. Formula 8 uses an imagearea of 2n+1 horizontal lines (2n+1 horizontal lines including a centralhorizontal line with the target pixel) as an area for which a symmetryevaluation value is to be calculated.

More specifically, the symmetry evaluation unit 222 of this modificationcalculates a symmetry evaluation value for each of the 2n+1 horizontallines using Formula 8. The symmetry evaluation unit 222 accumulates thesymmetry evaluation values calculated for the horizontal lines, anddivides the accumulated value by the number of horizontal lines toobtain the average. The symmetry evaluation unit 222 uses the obtainedaverage as the symmetry evaluation value SYM_(w)(i, j) of the targetpixel (i, j). In Formula 8, P_(x, y) indicates an image feature quantity(pixel value) at coordinates (x, y) in a feature-quantity extractionimage (the same applies hereafter).

$\begin{matrix}{\mspace{79mu} {{Formula}\mspace{14mu} 8}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left\lbrack {\frac{1}{w}*{\sum\limits_{k = 1}^{w}\; \begin{Bmatrix}{\left( {255 - {{P_{{i - k},m} - P_{{i + k},m}}}} \right)*} \\{P_{{i - k},m}*P_{{i + k},m}}\end{Bmatrix}}} \right\rbrack}}} & (8)\end{matrix}$

As written in Formula 8, the symmetry evaluation unit 222 calculates asymmetry evaluation value by setting the vertical width of a target areafor which a symmetry evaluation value is to be calculated as the widthcorresponding to the 2n+1 horizontal lines in total, which includes nlines located above the horizontal line including the target pixel and nlines located below the horizontal line including the target pixel. Whenthe target pixel is around the upper end or the lower end of an image,and fewer than n lines are above the horizontal line including thetarget pixel or fewer than n lines are below the horizontal lineincluding the target pixel, the width of the target area for which asymmetry evaluation value is to be calculated in the vertical directionmay be changed. When, for example, the target pixel is close to theupper end, and n1 lines (n1<n) (from the upper end of the image) areabove the horizontal line including the target pixel, an area including(n1+n+1) lines from the (j−n1)th line to the (j+n)th line may be set asthe target area for which a symmetry evaluation value is to becalculated.

The symmetry evaluation unit 222 of this modification performs theprocessing written as Formula 8 as in the above embodiments to obtainthe width w that returns the maximum value maxSYM as the symmetry widthwa.

The center detection unit 223, the object area detection unit 224, andthe superimposing unit 23 perform the same processing as described inthe above embodiments.

As described above, in the object detection system of the presentmodification, the symmetry evaluation unit 222 calculates a symmetryevaluation value for each of the 2n+1 horizontal lines using Formula 8.The symmetry evaluation unit 222 then accumulates the symmetryevaluation values calculated for the horizontal lines, and divides theaccumulated value by the number of horizontal lines to obtain theaverage, and uses the obtained average as the symmetry evaluation valueSYM_(w)(i, j) of the target pixel (i, j). In other words, the objectdetection system of the present modification uses an image area having apredetermined width in the vertical direction as a target for symmetryevaluation, and thus detects an area having high correlation in thevertical direction (e.g., a rectangular object having sides parallel tothe symmetrical axis or the central axis) with high accuracy.

The symmetry evaluation unit 222 may calculate the symmetry evaluationvalue SYM_(w)(i, j) using Formula 9 instead of Formula 8.

$\begin{matrix}{\mspace{79mu} {{Formula}\mspace{14mu} 9}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\lbrack {\left( {255 - {\frac{1}{{2\; n} + 1}{{{\sum\limits_{m = {j - n}}^{j + n}\; P_{{i - k},m}} - {\sum\limits_{m = {j - n}}^{j + n}\; P_{{i + k},m}}}}}} \right)*\left( {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; P_{{i - k},m}}} \right)*\left( {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; P_{{i + k},m}}} \right)} \right\rbrack}}} & (9)\end{matrix}$

In the processing written as Formula 8, the symmetry evaluation unit 222first evaluates symmetry in the horizontal direction, and thenaccumulates the symmetry evaluation values in the vertical direction andcalculates the average of the accumulated values to obtain the symmetryevaluation value SYM_(w)(i, j) of the target pixel (i, j).

In the processing in Formula 9, the symmetry evaluation unit 222 firstperforms the processing of accumulating values of each term in thevertical direction (average value calculation), and then evaluatessymmetry in the horizontal direction to calculate the symmetryevaluation value SYM_(w)(i, j) of the target pixel (i, j).

The symmetry evaluation unit 222 performs the processing written asFormula 9 to obtain the same processing results as obtained through theprocessing written as Formula 8.

Second Modification

A second modification of the first embodiment will now be described.

The object detection system according to the present modification hasthe same structure as described in the above embodiments. The presentembodiment will be described focusing on its differences from the aboveembodiments. The components that are the same as in the aboveembodiments are given the same reference numerals as those embodimentsand will not be described in detail.

A symmetry evaluation unit 222 of the present modification performs theprocessing written as Formula 10 below instead of Formula 8. Althoughthe weighting in the vertical direction is performed using a value of 1(without weighting) for each of all the horizontal lines in theprocessing written as Formula 10, the weighting is performed for eachhorizontal line using a weighting function d(m), which providesweighting in accordance with the function d(m) in the processing writtenas Formula 10.

$\begin{matrix}{\mspace{79mu} {{Formula}\mspace{14mu} 10}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left\lbrack {{d(m)}*\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \begin{Bmatrix}{\left( {255 - {{P_{{i - k},m} - P_{{i + k},m}}}} \right)*} \\{P_{{i - k},m}*P_{{i + k},m}}\end{Bmatrix}}} \right\rbrack}}} & (10)\end{matrix}$

For example, d(m)=n+1−|m−j|, or d(m)=c1×exp(−c2×(m−j) ̂ 2), where c1 isa coefficient (a positive coefficient) to determine the maximum value ofd(m), and c2 is a coefficient (a positive coefficient) used for rangeadjustment in the vertical direction. Through this processing, pixels inthe horizontal line including the target pixel (i, j) are weighted usinga larger value, and pixels in lines more distant from the horizontalline including the target pixel (i, j) are weighted using a smallervalue.

As a result, the symmetry evaluation value is calculated to be largerfor an area having high horizontal symmetry around a horizontal lineincluding a target pixel (i, j).

The symmetry evaluation unit 222 may use Formula 11 instead of Formula10 to calculate the symmetry evaluation value SYM_(w)(i, j).

$\begin{matrix}{\mspace{76mu} {{Formula}\mspace{14mu} 11}} & \; \\{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\lbrack {\left( {255 - {\frac{1}{{2\; n} + 1}{{{\sum\limits_{m = {j - n}}^{j + n}\; {{d(m)}*P_{{i - k},m}}} - {\sum\limits_{m = {j - n}}^{j + n}\; {{d(m)}*P_{{i + k},m}}}}}}} \right)*\left\{ {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left( {{d(m)}*P_{{i - k},m}} \right)}} \right\}*\left\{ {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left( {{d(m)}*P_{{i + k},m}} \right)}} \right\}} \right\rbrack}}} & (11)\end{matrix}$

In the processing written as Formula 10, the symmetry evaluation unit222 first evaluates symmetry in the horizontal direction, and thenaccumulates the symmetry evaluation values in the vertical direction andcalculates the average of the accumulated values to obtain the symmetryevaluation value SYM_(w)(i, j) of the target pixel (i, j).

In the processing written as Formula 11, the symmetry evaluation unit222 first performs the processing of accumulating values of each term inthe vertical direction (average value calculation), and then evaluatessymmetry in the horizontal direction to calculate the symmetryevaluation value SYM_(w)(i, j) of the target pixel (i, j).

The symmetry evaluation unit 222 performs the processing written asFormula 11 to obtain the same processing results as obtained through theprocessing written as Formula 10.

Second Embodiment

A second embodiment will now be described.

An object detection system according to the present embodiment has thesame structure as in the first embodiment. The present embodiment willbe hereafter described focusing on its differences from the firstembodiment. The components that are the same as in the above embodimentsare given the same reference numerals as those embodiments and will notbe described in detail.

In the first embodiment, the image feature quantity extraction unit 221uses an edge component as an image feature quantity of an object. Theimage feature quantity extraction unit 221 evaluates the symmetry usingthe edge component extracted as the image feature quantity.

In the second embodiment, the image feature quantity extraction unit 221uses a specific color component as an image feature quantity of anobject. For example, the image feature quantity extraction unit 221extracts a red component as an image feature quantity.

The operation of the object detection system according to the presentembodiment will now be described.

FIG. 8A shows example images generated in the steps of object detectionimplemented by the object detection system of the present embodiment.

In the present embodiment as well, the imaging apparatus 1 obtains thecaptured image 101 shown in FIG. 4A. The captured image 101 is thenprocessed in the object detection apparatus 2.

The image input unit 21 converts image signals defined in the RGB colorspace (an R-component signal, a G-component signal, and a B-componentsignal), which are input from the imaging apparatus 1, into signalsdefined in the YCbCr color space (a Y-component signal, a Cb-componentsignal, and a Cr-component signal). The image input unit 21 outputs theCr component signal (Cr-component image 202) to the image featurequantity extraction unit 221.

The image feature quantity extraction unit 221 subjects the Cr-componentimage (color difference red component image) 202, which is obtained bythe image input unit 21, to processing for extracting an image featurequantity. In the present embodiment, a physical quantity correlated withthe Cr component is used as the image feature quantity.

In the present embodiment, the image feature quantity extraction unit221 subjects the Cr-component image 202 to processing for enhancing theCr component (enhancement) to generate a feature-quantity extractionimage (an R-component image, or a Cr-component enhanced image) 203.

The processing performed subsequently is the same as described in thefirst embodiment. The same processing is performed except that the edgeimage in the first embodiment is replaced with the R-component image todetect a symmetrical object.

More specifically, the symmetry evaluation unit 222 subjects thefeature-quantity extraction image (the R-component image, or theCr-component enhanced image) 203 shown in FIG. 8C to the same processingas in the first embodiment to obtain symmetry evaluation map data(corresponding to the symmetry evaluation map image 204 shown in FIG.8D) and symmetry width map data (corresponding to the symmetry width mapimage 205 shown in FIG. 8E).

The center detection unit 223 subjects the symmetry evaluation map data(corresponding to the symmetry evaluation map image 204 shown in FIG.8D) to the same processing as in the first embodiment to obtain symmetrycenter map data (corresponding to the symmetry center map image 206shown in FIG. 8F).

The object area detection unit 224 subjects the symmetry center map data(corresponding to the symmetry center map image 206 shown in FIG. 8F)and the symmetry width map data (symmetry width map image 205) to thesame processing as in the first embodiment to detect the width of theobject in the horizontal direction, and to further detect the width ofthe object in the vertical direction.

In the same manner as in the first embodiment, the superimposing unit 23generates (superimposes) an image indicating an image area correspondingto the highly symmetrical object on an image output from the image inputunit 21 (an input image 101) based on information identifying an areacorresponding to the detected highly symmetrical object in the imageoutput from the object area detection unit 224.

The image generated (generated through superimposing) by thesuperimposing unit 23 is displayed by the display apparatus 3. FIG. 8Gshows an example of an image (output image 207) obtained by thesuperimposing unit 23. As shown in FIG. 8G, an area (an image area 230)including red image portions at horizontally symmetrical positions isdetected in an appropriate manner.

FIG. 8G shows the composite image 207 displayed by the display apparatus3 in the second embodiment. In the first embodiment, the symmetry of anobject is evaluated using the edge component, and thus the size of theentire vehicle is detected as the size of the object. Unlike this, thesymmetry of an object is evaluated by focusing on the red color of thetail lamps of the vehicle in the second embodiment. Thus, an areaincluding the tail lamps (an image area 230) is extracted.

As described above, the object detection system of the presentembodiment evaluates symmetry in a predetermined direction (horizontaldirection) in an image obtained by extracting an image feature quantityfrom an input image (captured image) (an image obtained by extracting aspecific color component) by varying the width of the image (the widthin the predetermined direction, which is the horizontal direction). Whendetermining that a predetermined image area has high symmetry, theobject detection system of the present embodiment can obtain the widthof the image area determined to have high symmetry in the predetermineddirection (horizontal direction) at the same time. In other words, theobject detection system of the present embodiment can extract theposition and the size of an object at one time using the symmetry of theobject included in the image.

The object detection system of the present embodiment can detect thecentral axis of a highly symmetrical object using evaluation data forsymmetry in a predetermined direction (horizontal direction). The objectdetection system of the present embodiment can thus detect a highlysymmetrical object with high accuracy using less arithmetic processing.Further, the object detection system of the present embodiment uses animage obtained by extracting a specific color component, and can thusdetect a highly symmetrical object containing a large quantity ofspecific color component with high accuracy.

The first embodiment and the second embodiment may be combined with eachother. An edge image is used to determine the vertical width andposition of an object and the horizontal width and position of theobject. Further, a specific color component image is used to determinethe vertical width and position of the object and the horizontal widthand position of the object. The averages of the values obtained fromthese images are then used to identify the width and the position of theobject. Alternatively, either the values obtained from the edge image orthe values obtained from the color component image may be weighted, andthe resulting values may be used to identify the position and the sizeof the object.

In the above embodiment, the processing focuses on the red component.The embodiment should not be limited to this example. The processing mayfocus on another color component (e.g., a green component or a bluecomponent) to detect an object having high symmetry for thepredetermined color.

The color space conversion for extracting an image feature quantityshould not be limited to the conversion described in the aboveembodiment, but may be other color space conversion for extracting asignal of a specific color component to extract a predetermined colorcomponent signal (color component image).

In the present embodiment, the same processing in the first modificationand the second modification of the first embodiment may be performed.

Other Embodiments

Some or all of the above embodiments and modifications may be combined.

In the above embodiments, an object having symmetry in the horizontaldirection is detected. In the same manner, an object having symmetry inthe vertical direction may also be detected. More specifically, theprocessing described in the first and second embodiments may beperformed by reversing the direction of processing between thehorizontal and vertical directions in the first and second embodimentsto detect an object having symmetry in the vertical direction.

Some or all of the functional units of the object detection apparatus 2according to the above embodiments may use a shared memory (e.g., aframe memory) in their processing.

When the imaging apparatus included in the object detection system ofeach of the above embodiments captures a color image at a predeterminedframe rate (e.g., 15 fps), the shared memory preferably has a capacitythat allows the object detection apparatus to process the color image atthe predetermined frame rate (e.g., 15 fps).

The above embodiments describe the processing to be performed on 8-bitdata (data of 0 to 255) (e.g., the processing written as the Formula 4or the like), the number of bits of data to be processed should not belimited to the number of bits (the possible range of data) describedabove.

Each block of the object detection system or the object detectionapparatus described in the above embodiments may be formed using asingle chip with a semiconductor device (an integrated circuit or afield programmable gate array (FPGA)), such as an LSI (large-scaleintegration) device, or some or all of the blocks of the objectdetection system and the object detection apparatus may be formed usinga single chip. Each block of the object detection system or the objectdetection apparatus described in the above embodiments may be formedusing a plurality of chips (semiconductor devices such as LSI devices)

All or part of the processes performed by the functional blocksdescribed in the above embodiments may be implemented using programs.All or part of the processes performed by the functional blocksdescribed in the above embodiments may be implemented by a centralprocessing unit (CPU) in a computer. The programs for these processesmay be stored in a storage device, such as a hard disk or a ROM, and maybe executed from the ROM or be read into a RAM and then executed.

The processes described in the above embodiments may be implemented byusing either hardware or software (including use of an operating system(OS), middleware, or a predetermined library), or may be implementedusing both software and hardware. When the object detection system andthe object detection apparatus of the above embodiments are implementedby hardware, the object detection system and the object detectionapparatus need timing adjustment for their processes. For ease ofexplanation, the timing adjustment associated with various signals usedin an actual hardware design is not described in detail in the aboveembodiments.

The processes described in the above embodiments may not be performed inthe order specified in the above embodiments. The order in which theprocesses are performed may be changed without departing from the scopeand the spirit of the invention.

The present invention may also include a computer program enabling acomputer to implement the method described in the above embodiments anda computer readable recording medium on which such a program isrecorded. The computer readable recording medium may be, for example, aflexible disk, a hard disk, a CD-ROM, an MO, a DVD, a DVD-ROM, aDVD-RAM, a Blu-ray disc (registered trademark), or a semiconductormemory.

The computer program may not be recorded on the recording medium but maybe transmitted with an electric communication line, a radio or cablecommunication line, or a network such as the Internet.

The specific structures described in the above embodiments are mereexamples of the present invention, and may be changed and modifiedvariously without departing from the scope and the spirit of theinvention.

REFERENCE SIGNS LIST

-   -   1000 object detection system    -   1 imaging apparatus    -   2 object detection apparatus    -   3 display apparatus (monitor)    -   21 image input unit    -   22 object detection unit    -   23 superimposing unit    -   221 image feature quantity extraction unit    -   222 symmetry evaluation unit    -   223 center detection unit    -   224 object area detection unit    -   23 superimposing unit

1. An object detection apparatus, comprising: an image input unitconfigured to receive an image; an image feature quantity extractionunit configured to extract a predetermined image feature quantity fromthe image to generate a feature-quantity extraction image; and asymmetry evaluation unit configured to set, for every processing targetpixel in the feature-quantity extraction image, a symmetry evaluationarea for evaluating symmetry in a first direction in the image in such amanner that the symmetry evaluation area is symmetrical with respect toa central axis in a second direction orthogonal to the first direction,and calculates a weighted value resulting from weighting a valueindicating a correlation value by an image feature quantity for a groupof pixels included in the symmetry evaluation area and locatedsymmetrical with respect to the central axis, the correlation valueindicating a correlation between image feature quantities of the groupof pixels, while varying a size of the symmetry evaluation area toobtain a symmetry evaluation value indicating a degree of the symmetryin the first direction.
 2. The object detection apparatus according toclaim 1, wherein the symmetry evaluation unit determines a width of thesymmetry in the first direction based on the size of the symmetryevaluation area corresponding to the symmetry evaluation valueindicating the highest symmetry in the first direction.
 3. The objectdetection apparatus according to claim 1, wherein the symmetryevaluation unit calculates the symmetry evaluation value for the targetpixel using the formula below: $\begin{matrix}{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\{ {\left( {{P\; \max} - {{P_{i - k} - P_{i + k}}}} \right)*P_{i - k}*P_{i + k}} \right\}}}} & \left( {{cl}\mspace{14mu} 3} \right)\end{matrix}$ while varying a value of 2w+1, where w is a naturalnumber, and where the symmetry evaluation value is larger when a pixelvalue in the feature-quantity extraction image is 0 or a positive valueand the image feature quantity is larger, Pmax is a predetermined valueequal to or greater than a maximum value of pixel values in thefeature-quantity extraction image, P_(i) is a pixel value of the targetpixel located at coordinates (i, j) in the feature-quantity extractionimage, P_(i−k) is a pixel value of a pixel located distant from thetarget pixel by k pixels to a first detection side that is one side inthe first direction, where k is a natural number, and P_(i+k) is a pixelvalue of a pixel located distant from the target pixel by k pixels to asecond detection side that is opposite to the first detection side inthe first direction, and 2w+1 is a width of the symmetry evaluation areain the first direction.
 4. The object detection apparatus according toclaim 1, wherein the symmetry evaluation unit calculates the symmetryevaluation value for the target pixel using the formula below:$\begin{matrix}{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left\lbrack {{d(m)}*\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \begin{Bmatrix}{\left( {{P\; \max} - {{P_{{i - k},m} - P_{{i + k},m}}}} \right)*} \\{P_{{i - k},m}*P_{{i + k},m}}\end{Bmatrix}}} \right\rbrack}}} & \left( {{cl}\mspace{14mu} 4\text{-}1} \right)\end{matrix}$ or using the formula below: $\begin{matrix}{{{SYM}_{w}\left( {i,j} \right)} = {\frac{1}{w}{\sum\limits_{k = 1}^{w}\; \left\lbrack {\left( {{P\; \max} - {\frac{1}{{2\; n} + 1}{{{\sum\limits_{m = {j - n}}^{j + n}\; {{d(m)}*P_{{i - k},m}}} - {\sum\limits_{m = {j - n}}^{j + n}\; {{d(m)}*P_{{i + k},m}}}}}}} \right)*\left\{ {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left( {{d(m)}*P_{{i - k},m}} \right)}} \right\}*\left\{ {\frac{1}{{2\; n} + 1}{\sum\limits_{m = {j - n}}^{j + n}\; \left( {{d(m)}*P_{{i + k},m}} \right)}} \right\}} \right\rbrack}}} & \left( {{cl}\mspace{14mu} 4\text{-}2} \right)\end{matrix}$ while varying a value of 2w+1, where w is a naturalnumber, and where the symmetry evaluation value is larger when a pixelvalue in the feature-quantity extraction image is 0 or a positive valueand the image feature quantity is larger, Pmax is a predetermined valueequal to or greater than a maximum value of pixel values in thefeature-quantity extraction image, and P_(i, j) is a pixel value of thetarget pixel located at coordinates (i, j) in the feature-quantityextraction image, 2w+1 is a width of the symmetry evaluation area in thefirst direction, 2n+1 is a width of the symmetry evaluation area in thesecond direction, and d(m) is a predetermined weighting function.
 5. Theobject detection apparatus according to claim 4, wherein the weightingfunction d(m) isd(m)=1,  (1)d(m)=n+1−|m−j|, or  (2)d(m)=c1×exp(−c2×(m−j)̂2),  (3) where c1 and c2 are predetermined positivecoefficients.
 6. The object detection apparatus according to claim 3,wherein the symmetry evaluation unit obtains a maximum value maxSYM ofthe symmetry evaluation value SYM_(w)(i, j), and determines the symmetrywidth based on the width 2w+1 of the symmetry evaluation area in thefirst direction corresponding to the maximum value of the symmetryevaluation value SYM_(w)(i, j).
 7. The object detection apparatusaccording to claim 1, wherein (1) when a value of a row i indicating aposition of the target pixel in the first direction is equal to or lessthan H/2, the symmetry evaluation unit calculates the symmetryevaluation value by varying a value of w within a range of 1≦w≦(i−1),where w is a half of the width of the symmetry evaluation area in thefirst direction and H is the number of pixels in the first direction ofthe image, and H is a natural number, and (2) when the value of the rowi indicating the position of the target pixel in the first direction isgreater than H/2, the symmetry evaluation unit calculates the symmetryevaluation value by varying the value of w within a range of 1≦w≦(H−i).8. The object detection apparatus according to claim 1, wherein theimage feature quantity is an edge intensity of the image.
 9. The objectdetection apparatus according to claim 1, wherein the image featurequantity is an intensity of a specific color component of the image. 10.A non-transitory computer-readable storage medium having stored thereona program enabling a computer to implement an object detection methodcomprising: receiving an image; extracting a predetermined image featurequantity from the image to generate a feature-quantity extraction image;and setting, for every processing target pixel in the feature-quantityextraction image, a symmetry evaluation area for evaluating symmetry ina first direction in the image in such a manner that the symmetryevaluation area is symmetrical with respect to a central axis in asecond direction orthogonal to the first direction, and calculating aweighted value resulting from weighting a value indicating a correlationvalue by an image feature quantity for a group of pixels included in thesymmetry evaluation area and located symmetrical with respect to thecentral axis, the correlation value indicating a correlation betweenimage feature quantities of the group of pixels, while varying a size ofthe symmetry evaluation area to obtain a symmetry evaluation valueindicating a degree of the symmetry in the first direction.